Overview

Dataset statistics

Number of variables13
Number of observations18249
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric9
Categorical3

Alerts

region has a high cardinality: 54 distinct values High cardinality
AveragePrice is highly correlated with Total Volume and 6 other fieldsHigh correlation
Total Volume is highly correlated with AveragePrice and 7 other fieldsHigh correlation
4046 is highly correlated with AveragePrice and 7 other fieldsHigh correlation
4225 is highly correlated with AveragePrice and 7 other fieldsHigh correlation
4770 is highly correlated with AveragePrice and 7 other fieldsHigh correlation
Total Bags is highly correlated with AveragePrice and 7 other fieldsHigh correlation
Small Bags is highly correlated with AveragePrice and 7 other fieldsHigh correlation
Large Bags is highly correlated with AveragePrice and 7 other fieldsHigh correlation
XLarge Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
Total Volume is highly correlated with 4046 and 6 other fieldsHigh correlation
4046 is highly correlated with Total Volume and 6 other fieldsHigh correlation
4225 is highly correlated with Total Volume and 6 other fieldsHigh correlation
4770 is highly correlated with Total Volume and 6 other fieldsHigh correlation
Total Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
Small Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
Large Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
XLarge Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
Total Volume is highly correlated with 4046 and 6 other fieldsHigh correlation
4046 is highly correlated with Total Volume and 4 other fieldsHigh correlation
4225 is highly correlated with Total Volume and 4 other fieldsHigh correlation
4770 is highly correlated with Total Volume and 5 other fieldsHigh correlation
Total Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
Small Bags is highly correlated with Total Volume and 5 other fieldsHigh correlation
Large Bags is highly correlated with Total Volume and 1 other fieldsHigh correlation
XLarge Bags is highly correlated with Total Volume and 3 other fieldsHigh correlation
AveragePrice is highly correlated with type and 1 other fieldsHigh correlation
Total Volume is highly correlated with 4046 and 7 other fieldsHigh correlation
4046 is highly correlated with Total Volume and 7 other fieldsHigh correlation
4225 is highly correlated with Total Volume and 7 other fieldsHigh correlation
4770 is highly correlated with Total Volume and 7 other fieldsHigh correlation
Total Bags is highly correlated with Total Volume and 7 other fieldsHigh correlation
Small Bags is highly correlated with Total Volume and 7 other fieldsHigh correlation
Large Bags is highly correlated with Total Volume and 7 other fieldsHigh correlation
XLarge Bags is highly correlated with Total Volume and 6 other fieldsHigh correlation
type is highly correlated with AveragePriceHigh correlation
region is highly correlated with AveragePrice and 7 other fieldsHigh correlation
region is uniformly distributed Uniform
4046 has 242 (1.3%) zeros Zeros
4770 has 5497 (30.1%) zeros Zeros
Large Bags has 2370 (13.0%) zeros Zeros
XLarge Bags has 12048 (66.0%) zeros Zeros

Reproduction

Analysis started2022-02-01 09:55:59.471909
Analysis finished2022-02-01 09:56:31.346799
Duration31.87 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

Distinct169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
Minimum2015-01-04 00:00:00
Maximum2018-03-25 00:00:00
2022-02-01T15:26:31.591502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:31.872417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AveragePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct259
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.40597841
Minimum0.44
Maximum3.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:32.136437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile0.83
Q11.1
median1.37
Q31.66
95-th percentile2.11
Maximum3.25
Range2.81
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.4026765555
Coefficient of variation (CV)0.2864030861
Kurtosis0.3251958507
Mean1.40597841
Median Absolute Deviation (MAD)0.28
Skewness0.5803027379
Sum25657.7
Variance0.1621484083
MonotonicityNot monotonic
2022-02-01T15:26:32.398459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.15202
 
1.1%
1.18199
 
1.1%
1.08194
 
1.1%
1.26193
 
1.1%
1.13192
 
1.1%
0.98189
 
1.0%
1.19188
 
1.0%
1.36187
 
1.0%
1.59186
 
1.0%
1.43185
 
1.0%
Other values (249)16334
89.5%
ValueCountFrequency (%)
0.441
 
< 0.1%
0.461
 
< 0.1%
0.481
 
< 0.1%
0.492
 
< 0.1%
0.515
< 0.1%
0.523
 
< 0.1%
0.536
< 0.1%
0.547
< 0.1%
0.553
 
< 0.1%
0.5612
0.1%
ValueCountFrequency (%)
3.251
< 0.1%
3.171
< 0.1%
3.121
< 0.1%
3.051
< 0.1%
3.041
< 0.1%
3.031
< 0.1%
32
< 0.1%
2.992
< 0.1%
2.971
< 0.1%
2.961
< 0.1%

Total Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18237
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850644.013
Minimum84.56
Maximum62505646.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:32.676165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum84.56
5-th percentile2371.862
Q110838.58
median107376.76
Q3432962.29
95-th percentile3716315.41
Maximum62505646.52
Range62505561.96
Interquartile range (IQR)422123.71

Descriptive statistics

Standard deviation3453545.355
Coefficient of variation (CV)4.059918488
Kurtosis92.10445778
Mean850644.013
Median Absolute Deviation (MAD)102962.47
Skewness9.007687479
Sum1.552340259 × 1010
Variance1.192697552 × 1013
MonotonicityNot monotonic
2022-02-01T15:26:32.926847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4103.972
 
< 0.1%
3529.442
 
< 0.1%
46602.162
 
< 0.1%
13234.042
 
< 0.1%
3713.492
 
< 0.1%
19634.242
 
< 0.1%
3288.852
 
< 0.1%
9465.992
 
< 0.1%
2038.992
 
< 0.1%
2858.312
 
< 0.1%
Other values (18227)18229
99.9%
ValueCountFrequency (%)
84.561
< 0.1%
379.821
< 0.1%
385.551
< 0.1%
419.981
< 0.1%
472.821
< 0.1%
482.261
< 0.1%
515.011
< 0.1%
530.961
< 0.1%
542.851
< 0.1%
561.11
< 0.1%
ValueCountFrequency (%)
62505646.521
< 0.1%
61034457.11
< 0.1%
52288697.891
< 0.1%
47293921.61
< 0.1%
46324529.71
< 0.1%
44655461.511
< 0.1%
43409835.751
< 0.1%
43167806.091
< 0.1%
42939821.551
< 0.1%
42867608.541
< 0.1%

4046
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17702
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293008.4245
Minimum0
Maximum22743616.17
Zeros242
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:33.195947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.6
Q1854.07
median8645.3
Q3111020.2
95-th percentile1263359.678
Maximum22743616.17
Range22743616.17
Interquartile range (IQR)110166.13

Descriptive statistics

Standard deviation1264989.082
Coefficient of variation (CV)4.317244747
Kurtosis86.80911256
Mean293008.4245
Median Absolute Deviation (MAD)8616.69
Skewness8.648219757
Sum5347110739
Variance1.600197377 × 1012
MonotonicityNot monotonic
2022-02-01T15:26:33.470968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0242
 
1.3%
310
 
0.1%
48
 
< 0.1%
1.248
 
< 0.1%
18
 
< 0.1%
1.257
 
< 0.1%
67
 
< 0.1%
1.216
 
< 0.1%
1.35
 
< 0.1%
1.275
 
< 0.1%
Other values (17692)17943
98.3%
ValueCountFrequency (%)
0242
1.3%
18
 
< 0.1%
1.131
 
< 0.1%
1.193
 
< 0.1%
1.21
 
< 0.1%
1.216
 
< 0.1%
1.225
 
< 0.1%
1.231
 
< 0.1%
1.248
 
< 0.1%
1.257
 
< 0.1%
ValueCountFrequency (%)
22743616.171
< 0.1%
21620180.91
< 0.1%
18933038.041
< 0.1%
17787611.931
< 0.1%
17076650.821
< 0.1%
16573573.781
< 0.1%
16529797.61
< 0.1%
16383685.071
< 0.1%
16215328.751
< 0.1%
16000107.81
< 0.1%

4225
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18103
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295154.5684
Minimum0
Maximum20470572.61
Zeros61
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:34.048860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile103.614
Q13008.78
median29061.02
Q3150206.86
95-th percentile1303657.658
Maximum20470572.61
Range20470572.61
Interquartile range (IQR)147198.08

Descriptive statistics

Standard deviation1204120.401
Coefficient of variation (CV)4.079626508
Kurtosis91.94902197
Mean295154.5684
Median Absolute Deviation (MAD)28521.3
Skewness8.942465608
Sum5386275718
Variance1.44990594 × 1012
MonotonicityNot monotonic
2022-02-01T15:26:34.280572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
0.3%
177.873
 
< 0.1%
215.363
 
< 0.1%
1.33
 
< 0.1%
1.263
 
< 0.1%
94.743
 
< 0.1%
13.62
 
< 0.1%
20.322
 
< 0.1%
35898.692
 
< 0.1%
6973.512
 
< 0.1%
Other values (18093)18165
99.5%
ValueCountFrequency (%)
061
0.3%
1.263
 
< 0.1%
1.282
 
< 0.1%
1.33
 
< 0.1%
1.311
 
< 0.1%
1.322
 
< 0.1%
1.641
 
< 0.1%
2.391
 
< 0.1%
2.41
 
< 0.1%
2.481
 
< 0.1%
ValueCountFrequency (%)
20470572.611
< 0.1%
20445501.031
< 0.1%
20328161.551
< 0.1%
18956479.741
< 0.1%
17896391.61
< 0.1%
16602589.041
< 0.1%
16054083.861
< 0.1%
15899858.371
< 0.1%
14888077.691
< 0.1%
14437190.031
< 0.1%

4770
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12071
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22839.73599
Minimum0
Maximum2546439.11
Zeros5497
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:34.562433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median184.99
Q36243.42
95-th percentile106156.574
Maximum2546439.11
Range2546439.11
Interquartile range (IQR)6243.42

Descriptive statistics

Standard deviation107464.0684
Coefficient of variation (CV)4.705136192
Kurtosis132.5634409
Mean22839.73599
Median Absolute Deviation (MAD)184.99
Skewness10.15939563
Sum416802342.1
Variance1.1548526 × 1010
MonotonicityNot monotonic
2022-02-01T15:26:34.844150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05497
30.1%
2.667
 
< 0.1%
3.327
 
< 0.1%
10.976
 
< 0.1%
1.596
 
< 0.1%
1.646
 
< 0.1%
1.66
 
< 0.1%
2.745
 
< 0.1%
1.665
 
< 0.1%
1.185
 
< 0.1%
Other values (12061)12699
69.6%
ValueCountFrequency (%)
05497
30.1%
0.831
 
< 0.1%
13
 
< 0.1%
1.011
 
< 0.1%
1.091
 
< 0.1%
1.111
 
< 0.1%
1.121
 
< 0.1%
1.151
 
< 0.1%
1.161
 
< 0.1%
1.185
 
< 0.1%
ValueCountFrequency (%)
2546439.111
< 0.1%
1993645.361
< 0.1%
1896149.51
< 0.1%
1880231.381
< 0.1%
1811090.711
< 0.1%
1800065.571
< 0.1%
1773088.871
< 0.1%
1770948.091
< 0.1%
1761343.081
< 0.1%
1753852.611
< 0.1%

Total Bags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18097
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239639.2021
Minimum0
Maximum19373134.37
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:35.138171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile628.89
Q15088.64
median39743.83
Q3110783.37
95-th percentile1005478.892
Maximum19373134.37
Range19373134.37
Interquartile range (IQR)105694.73

Descriptive statistics

Standard deviation986242.3992
Coefficient of variation (CV)4.115530309
Kurtosis112.2721565
Mean239639.2021
Median Absolute Deviation (MAD)37299.96
Skewness9.75607167
Sum4373175798
Variance9.7267407 × 1011
MonotonicityNot monotonic
2022-02-01T15:26:35.418192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015
 
0.1%
9905
 
< 0.1%
3005
 
< 0.1%
5504
 
< 0.1%
266.674
 
< 0.1%
916.674
 
< 0.1%
286.673
 
< 0.1%
263.333
 
< 0.1%
196.673
 
< 0.1%
2603
 
< 0.1%
Other values (18087)18200
99.7%
ValueCountFrequency (%)
015
0.1%
3.091
 
< 0.1%
3.111
 
< 0.1%
3.191
 
< 0.1%
3.331
 
< 0.1%
6.141
 
< 0.1%
6.181
 
< 0.1%
6.241
 
< 0.1%
6.361
 
< 0.1%
7.021
 
< 0.1%
ValueCountFrequency (%)
19373134.371
< 0.1%
16394524.111
< 0.1%
16298296.291
< 0.1%
15972492.071
< 0.1%
15804696.311
< 0.1%
15102426.941
< 0.1%
15051877.141
< 0.1%
14894893.81
< 0.1%
14504209.371
< 0.1%
14440611.51
< 0.1%

Small Bags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17321
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182194.6867
Minimum0
Maximum13384586.8
Zeros159
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:35.701214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256.67
Q12849.42
median26362.82
Q383337.67
95-th percentile768147.228
Maximum13384586.8
Range13384586.8
Interquartile range (IQR)80488.25

Descriptive statistics

Standard deviation746178.515
Coefficient of variation (CV)4.095500964
Kurtosis107.0128851
Mean182194.6867
Median Absolute Deviation (MAD)25599.49
Skewness9.540659982
Sum3324870838
Variance5.567823762 × 1011
MonotonicityNot monotonic
2022-02-01T15:26:35.965159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0159
 
0.9%
203.3311
 
0.1%
223.3310
 
0.1%
533.3310
 
0.1%
123.338
 
< 0.1%
196.678
 
< 0.1%
708
 
< 0.1%
103.338
 
< 0.1%
216.678
 
< 0.1%
208
 
< 0.1%
Other values (17311)18011
98.7%
ValueCountFrequency (%)
0159
0.9%
2.521
 
< 0.1%
2.571
 
< 0.1%
2.731
 
< 0.1%
2.791
 
< 0.1%
2.953
 
< 0.1%
2.961
 
< 0.1%
3.061
 
< 0.1%
3.091
 
< 0.1%
3.111
 
< 0.1%
ValueCountFrequency (%)
13384586.81
< 0.1%
12567155.581
< 0.1%
12540327.191
< 0.1%
11712807.191
< 0.1%
11392828.891
< 0.1%
11228049.631
< 0.1%
11112405.611
< 0.1%
10844852.221
< 0.1%
10832907.441
< 0.1%
10666942.781
< 0.1%

Large Bags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15082
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54338.08814
Minimum0
Maximum5719096.61
Zeros2370
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:36.254232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1127.47
median2647.71
Q322029.25
95-th percentile195699.768
Maximum5719096.61
Range5719096.61
Interquartile range (IQR)21901.78

Descriptive statistics

Standard deviation243965.9645
Coefficient of variation (CV)4.489778218
Kurtosis117.999481
Mean54338.08814
Median Absolute Deviation (MAD)2647.71
Skewness9.796454599
Sum991615770.5
Variance5.951939186 × 1010
MonotonicityNot monotonic
2022-02-01T15:26:36.527252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02370
 
13.0%
3.33187
 
1.0%
6.6778
 
0.4%
1047
 
0.3%
4.4438
 
0.2%
13.3328
 
0.2%
16.6718
 
0.1%
26.6718
 
0.1%
6.6618
 
0.1%
2014
 
0.1%
Other values (15072)15433
84.6%
ValueCountFrequency (%)
02370
13.0%
0.971
 
< 0.1%
1.31
 
< 0.1%
1.331
 
< 0.1%
1.382
 
< 0.1%
1.441
 
< 0.1%
1.481
 
< 0.1%
1.551
 
< 0.1%
1.561
 
< 0.1%
1.621
 
< 0.1%
ValueCountFrequency (%)
5719096.611
< 0.1%
4324231.191
< 0.1%
4081397.721
< 0.1%
4023485.041
< 0.1%
3988101.741
< 0.1%
3917569.951
< 0.1%
3789722.91
< 0.1%
3618270.751
< 0.1%
3544729.391
< 0.1%
3434846.781
< 0.1%

XLarge Bags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5588
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3106.426507
Minimum0
Maximum551693.65
Zeros12048
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size142.7 KiB
2022-02-01T15:26:36.830274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3132.5
95-th percentile12058.452
Maximum551693.65
Range551693.65
Interquartile range (IQR)132.5

Descriptive statistics

Standard deviation17692.89465
Coefficient of variation (CV)5.695578058
Kurtosis233.6026119
Mean3106.426507
Median Absolute Deviation (MAD)0
Skewness13.13975069
Sum56689177.33
Variance313038521.2
MonotonicityNot monotonic
2022-02-01T15:26:37.080594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012048
66.0%
3.3329
 
0.2%
6.6716
 
0.1%
1.1115
 
0.1%
512
 
0.1%
109
 
< 0.1%
16.678
 
< 0.1%
2.227
 
< 0.1%
1506
 
< 0.1%
13.336
 
< 0.1%
Other values (5578)6093
33.4%
ValueCountFrequency (%)
012048
66.0%
11
 
< 0.1%
1.1115
 
0.1%
1.261
 
< 0.1%
1.31
 
< 0.1%
1.381
 
< 0.1%
1.412
 
< 0.1%
1.451
 
< 0.1%
1.474
 
< 0.1%
1.492
 
< 0.1%
ValueCountFrequency (%)
551693.651
< 0.1%
454343.651
< 0.1%
390478.731
< 0.1%
387400.221
< 0.1%
377661.061
< 0.1%
373523.471
< 0.1%
347390.141
< 0.1%
328589.091
< 0.1%
326348.151
< 0.1%
321033.231
< 0.1%

type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
conventional
9126 
organic
9123 

Length

Max length12
Median length12
Mean length9.500410981
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconventional
2nd rowconventional
3rd rowconventional
4th rowconventional
5th rowconventional

Common Values

ValueCountFrequency (%)
conventional9126
50.0%
organic9123
50.0%

Length

2022-02-01T15:26:37.296379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T15:26:37.416398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
conventional9126
50.0%
organic9123
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
2017
5722 
2016
5616 
2015
5615 
2018
1296 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
20175722
31.4%
20165616
30.8%
20155615
30.8%
20181296
 
7.1%

Length

2022-02-01T15:26:37.579087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T15:26:38.027120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
20175722
31.4%
20165616
30.8%
20155615
30.8%
20181296
 
7.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

region
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
Albany
 
338
Sacramento
 
338
Northeast
 
338
NorthernNewEngland
 
338
Orlando
 
338
Other values (49)
16559 

Length

Max length19
Median length9
Mean length10.29535865
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbany
2nd rowAlbany
3rd rowAlbany
4th rowAlbany
5th rowAlbany

Common Values

ValueCountFrequency (%)
Albany338
 
1.9%
Sacramento338
 
1.9%
Northeast338
 
1.9%
NorthernNewEngland338
 
1.9%
Orlando338
 
1.9%
Philadelphia338
 
1.9%
PhoenixTucson338
 
1.9%
Pittsburgh338
 
1.9%
Plains338
 
1.9%
Portland338
 
1.9%
Other values (44)14869
81.5%

Length

2022-02-01T15:26:38.192134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
albany338
 
1.9%
denver338
 
1.9%
midsouth338
 
1.9%
baltimorewashington338
 
1.9%
boise338
 
1.9%
boston338
 
1.9%
buffalorochester338
 
1.9%
california338
 
1.9%
charlotte338
 
1.9%
chicago338
 
1.9%
Other values (44)14869
81.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-01T15:26:28.036772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:10.243396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:12.309205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:14.541618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:16.788575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:19.165915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:21.251163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:23.725678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:25.969885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:28.286489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:10.479414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:12.548217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:14.807638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:17.341801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:19.407523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:21.490191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:23.974698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:26.186903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:28.529511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:10.674204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:12.799960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:15.062715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:17.580497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:19.624269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:21.721199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:24.204715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:26.440608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:29.056321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:10.854030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:13.027416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:15.298413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:17.785508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:19.858213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:21.957217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:24.460302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:26.649624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:29.299342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:11.225801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:13.250103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:15.519088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:17.987527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:20.086229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:22.200280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:24.690985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:26.863383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:29.549840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:11.451906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:13.471137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:15.763111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:18.221418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:20.326165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:22.437628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:24.931805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:27.102403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:29.796219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:11.682921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:13.706147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:16.038477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:18.485422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:20.558885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:22.697397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:25.191833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:27.344420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:30.025234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:11.909945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:14.001419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:16.316505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:18.701440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:20.820347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:23.243094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:25.470850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:27.573297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:30.272717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:12.098511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:14.259910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:16.560771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:18.944458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:21.035147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:23.489953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:25.720864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T15:26:27.802006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-01T15:26:38.406132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-01T15:26:38.722105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T15:26:39.010127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T15:26:39.292151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-01T15:26:39.517001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T15:26:30.624277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T15:26:31.096092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
02015-12-271.3364236.621036.7454454.8548.168696.878603.6293.250.0conventional2015Albany
12015-12-201.3554876.98674.2844638.8158.339505.569408.0797.490.0conventional2015Albany
22015-12-130.93118220.22794.70109149.67130.508145.358042.21103.140.0conventional2015Albany
32015-12-061.0878992.151132.0071976.4172.585811.165677.40133.760.0conventional2015Albany
42015-11-291.2851039.60941.4843838.3975.786183.955986.26197.690.0conventional2015Albany
52015-11-221.2655979.781184.2748067.9943.616683.916556.47127.440.0conventional2015Albany
62015-11-150.9983453.761368.9273672.7293.268318.868196.81122.050.0conventional2015Albany
72015-11-080.98109428.33703.75101815.3680.006829.226266.85562.370.0conventional2015Albany
82015-11-011.0299811.421022.1587315.5785.3411388.3611104.53283.830.0conventional2015Albany
92015-10-251.0774338.76842.4064757.44113.008625.928061.47564.450.0conventional2015Albany

Last rows

DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
182392018-03-111.5622128.422162.673194.258.9316762.5716510.32252.250.0organic2018WestTexNewMexico
182402018-03-041.5417393.301832.241905.570.0013655.4913401.93253.560.0organic2018WestTexNewMexico
182412018-02-251.5718421.241974.262482.650.0013964.3313698.27266.060.0organic2018WestTexNewMexico
182422018-02-181.5617597.121892.051928.360.0013776.7113553.53223.180.0organic2018WestTexNewMexico
182432018-02-111.5715986.171924.281368.320.0012693.5712437.35256.220.0organic2018WestTexNewMexico
182442018-02-041.6317074.832046.961529.200.0013498.6713066.82431.850.0organic2018WestTexNewMexico
182452018-01-281.7113888.041191.703431.500.009264.848940.04324.800.0organic2018WestTexNewMexico
182462018-01-211.8713766.761191.922452.79727.949394.119351.8042.310.0organic2018WestTexNewMexico
182472018-01-141.9316205.221527.632981.04727.0110969.5410919.5450.000.0organic2018WestTexNewMexico
182482018-01-071.6217489.582894.772356.13224.5312014.1511988.1426.010.0organic2018WestTexNewMexico